---
title: "Model Mieszkań Wrocław"
author: "Igor Gryzlo"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)
library(corrplot)
library(readxl)
Dane <- as.data.frame(read_excel("C:/Users/bezna/Documents/UEK/Licencjat/Wizualizacja Danych/Igor/17.04/Zadanie Dashboard/Nowy folder/daneig.xlsx"))
```

Strona nr.1
=======================================================================

Row
-----------------------------------------------------------------------

### Wykres Ceny i Lat użytkowania Mieszkania (1)

```{r}
plot(Dane$Cena~Dane$`Lata uzytkowania`)
```


### Wykres ceny i odległości od centrum mieszkania

```{r}
plot(Dane$Cena~Dane$`Odległosc od centrum`)
Model1<-lm(Dane$Cena~Dane$`Odległosc od centrum`)
abline(Model1$coef,lty=5)

```

Row
-----------------------------------------------------------------------

### Wykres Przedstawiający zależności wszystkich parametrów

```{r}
colnames(Dane)<-c("Cena","Lata","Odleglosc","Metraz")
corrplot(cor(Dane))
```

### Wykres Kwantyl Kwantyl

```{r}
Model <- lm(Cena~Lata+Odleglosc+Metraz,data=Dane)
par(mfrow=c(1,1))
qqnorm(rstudent(Model),ylab="Studentized residuals")
abline(0,1)

```

### HeatMap

```{r}
heatmap(abs(cor(Dane)),symm=T)

```

Strona nr.2
=======================================================================

Row
-----------------------------------------------------------------------

### Wykres Lat i ceny

```{r}
p <- ggplot(Dane, aes(x=Lata, y=Cena)) +
        	geom_point(shape=1)  	# Use hollow circles
ggplotly(p)

```

### Wykres 6

```{r}
p <- ggplot(Dane, aes(x=Lata, y=Cena)) +
        	geom_point(shape=1)+  	# Use hollow circles
        	geom_smooth(method=lm, color="red")   # Add linear regression line
ggplotly(p)

```

Row
-----------------------------------------------------------------------

### Wykres 7

```{r}
p <- ggplot(Dane, aes(x=Lata, y=Cena)) +
        	geom_point(shape=1)+  	# Use hollow circles
        	geom_smooth()   # Add a loess smoothed fit curve with confidence
ggplotly(p)

```

### Wykres 8

```{r}
p <- ggplot(Dane, aes(x=Lata, y=Cena)) +
 	geom_point(alpha = 0.5) +
 	geom_density_2d() +
 	theme(panel.background = element_rect(fill = '#ffffff'))
ggplotly(p)
```

Strona nr.3
=======================================================================

Row
-----------------------------------------------------------------------

### Lista danych o mieszkaniach

```{r}
 #Source: https://www.htmlwidgets.org/showcase_datatables.html
library(DT)
datatable(Dane, options = list(pageLength = 10))
```

### Wykres mieszkań 3D

```{r}

library(threejs)
z <- Dane$Cena
x <- Dane$Lata
y <- Dane$Odleglosc
scatterplot3js(x,y,z, color=rainbow(length(z)))

```



##############################################

Strona nr.4
=======================================================================

Row
-----------------------------------------------------------------------

### scatterplot3d()
```{r}
library("scatterplot3d")
scatterplot3d(Dane$Lata,Dane$Metraz,Dane$Price)

```
Row
-----------------------------------------------------------------------

### hist3D
```{r}
library("plot3D")
library("MASS")
est_jadrowy<-kde2d(Dane$Odleglosc,Dane$Cena)
par(mfrow=c(1,1))
hist3D(x=est_jadrowy$x,y=est_jadrowy$y,z=est_jadrowy$z,phi = 0, theta = 100,main = "Mieszkania Wrocław",xlab="Odleglosc od centrum",ylab="Cena")
```


##############################################